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--- |
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license: mit |
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datasets: |
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- QizhiPei/BioT5_finetune_dataset |
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language: |
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- en |
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--- |
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## Example Usage |
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```python |
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from transformers import T5Tokenizer, T5ForConditionalGeneration |
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tokenizer = T5Tokenizer.from_pretrained("QizhiPei/biot5-base-text2mol", model_max_length=512) |
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model = T5ForConditionalGeneration.from_pretrained('QizhiPei/biot5-base-text2mol') |
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task_definition = 'Definition: You are given a molecule description in English. Your job is to generate the molecule SELFIES that fits the description.\n\n' |
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text_input = 'The molecule is a monocarboxylic acid anion obtained by deprotonation of the carboxy and sulfino groups of 3-sulfinopropionic acid. Major microspecies at pH 7.3 It is an organosulfinate oxoanion and a monocarboxylic acid anion. It is a conjugate base of a 3-sulfinopropionic acid.' |
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task_input = f'Now complete the following example -\nInput: {text_input}\nOutput: ' |
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model_input = task_definition + task_input |
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input_ids = tokenizer(model_input, return_tensors="pt").input_ids |
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generation_config = model.generation_config |
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generation_config.max_length = 512 |
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generation_config.num_beams = 1 |
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outputs = model.generate(input_ids, generation_config=generation_config) |
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output_selfies = tokenizer.decode(outputs[0], skip_special_tokens=True).replace(' ', '') |
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print(output_selfies) |
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import selfies as sf |
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output_smiles = sf.decoder(output_selfies) |
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print(output_smiles) |
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``` |
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## References |
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For more information, please refer to our paper and GitHub repository. |
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Paper: [BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations](https://arxiv.org/abs/2310.07276) |
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GitHub: [BioT5](https://github.com/QizhiPei/BioT5) |
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Authors: *Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, and Rui Yan* |
